Network traffic recovery from link-load measurements using tensor triple decomposition strategy for third-order traffic tensors

Network traffic data is the pivot of input in many network tasks but its direct measurement can be insufferably costly. In this paper, we propose a network traffic recovery method which only requires the conveniently measurable link-load traffics. We arrange the traffic data as a third-order tensor...

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Bibliographic Details
Published in:	Journal of computational and applied mathematics Vol. 447; p. 115901
Main Authors:	Ming, Zhenyu, Qin, Zhenzhi, Zhang, Liping, Xu, Yanwei, Qi, Liqun
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.09.2024
Subjects:	BB gradient algorithm Network traffic recovery Triple decomposition 65K05 49M29 Network traffic recovery 68M10 15A03 BB gradient algorithm Triple decomposition
ISSN:	0377-0427
Online Access:	Get full text
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Summary:	Network traffic data is the pivot of input in many network tasks but its direct measurement can be insufferably costly. In this paper, we propose a network traffic recovery method which only requires the conveniently measurable link-load traffics. We arrange the traffic data as a third-order tensor and utilize the triple decomposition technique proposed very recently by Qi et al. (2021). The studied model is a differentialble unconstrained minimization problem, which can be efficiently solved by a Barzilai–Borwein (BB) gradient algorithm. We prove that the generated iteration sequence can globally converge to a certain stationary point of the objective function. The numerical simulations on three open-source traffic datasets demonstrate the superiority of our method in comparison with other state-of-the-art algorithms.
ISSN:	0377-0427
DOI:	10.1016/j.cam.2024.115901